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The purpose of this paper is to review and analyze the perishable inventory models along various dimensions such as its evolution, scope, demand, shelf life, replenishment policy, modeling techniques and research gaps.

In total, 418 relevant and scholarly articles of various researchers and practitioners during 1990-2016 were reviewed. They were critically analyzed along author profile, nature of perishability, research contributions of different countries, publication along time, research methodologies adopted, etc. to draw fruitful conclusions. The future research for perishable inventory modeling was also discussed and suggested.

There are plethora of perishable inventory studies with divergent objectives and scope. Besides demand and perishable rate in perishable inventory models, other factors such as price discount, allow shortage or not, inflation, time value of money and so on were found to be combined to make it more realistic. The modeling of inventory systems with two or more perishable items is limited. The multi-echelon inventory with centralized decision and information sharing is acquiring lot of importance because of supply chain integration in the competitive market.

Only peer-reviewed journals and conference papers were analyzed, whereas the manuals, reports, white papers and blood-related articles were excluded. Clustering of literature revealed that future studies should focus on stochastic modeling.

Stress had been laid to identify future research gaps that will help in developing realistic models. The present work will form a guideline to choose the appropriate methodology(s) and mathematical technique(s) in different situations with perishable inventory.

The current review analyzed 419 research papers available in the literature on perishable inventory modeling to summarize its current status and identify its potential future directions. Also the future research gaps were uncovered. This systemic review is strongly felt to fill the gap in the perishable inventory literature and help in formulating effective strategies to design of an effective and efficient inventory management system for perishable items.

Managing perishable inventories is a trade‐off of shortages and lost sales against wastage. This paper aims to identify what drives good management of perishables within the supply chain using the example of blood inventory management in hospitals.

Seven case studies with hospital transfusion laboratories in the UK blood supply chain were carried out in order to explore how perishable inventories are managed. The case studies identify drivers for good performance in perishable inventories.

Six recommendations are developed for how managers can improve perishable inventory performance. These are based around simple management procedures implemented by experienced staff. The case studies develop three propositions that recommend how inventory theory should be embedded in practice.

This research demonstrates that managerial changes and training issues have a significant impact on waste reduction and inventory management performance in perishable supply chains. However, as the case studies focus on the blood supply chain, some caution needs to be applied in generalising these findings beyond the specific context studied.

A multi‐disciplinary approach, combining awareness of the importance of the dynamics of the whole supply chain with good skill and experience, leads to new thinking, which enables staff to make better inventory decisions resulting in better performance and reduced wastage. Managerial changes and training are critical for good inventory performance.

Literature suggests that sophisticated and complex inventory models will drive performance; however, in practice a combination of basic well‐grounded inventory theory with simple management procedures carried out by experienced staff leads to better performance.

This research proposes a decision framework for using non-financial measures to define a replenishment policy for perishable health products. These products are perishable and substitutable by nature and create complexities for managing inventory. Instead of a financial measure, numerous measures should be considered and balanced to meet business objectives and enhance inventory management.

This research applies a multi-methodological approach and develops a framework that integrates discrete event simulation (DES), analytic hierarchy process (AHP) and data envelopment analysis (DEA) techniques to define the most favourable replenishment policy using non-financial measures.

The integration framework performs well as illustrated in the numerical example; outcomes from the framework are comparable to those generated using a traditional, financial measures-based, approach. This research demonstrates that it is feasible to adopt non-financial performance measures to define a replenishment policy and evaluate performance.

The framework, thus, prioritises non-financial measures and addresses issues of lacking information sharing and employee involvement to enhance hospitals' performance while minimising costs. The non-financial measures improve cross-functional communication while supporting simpler transformations from high-level strategies to daily operational targets.

Perishable inventory management for the grocery sector has become more challenging with extended omnichannel activities and emerging consumer expectations. This paper aims to identify and formalize key performance measures of omnichannel perishable inventory management (OCPI) and explore the influence of operational and market-related factors on these measures.

The inductive approach of this research synthesizes three performance measures (product waste, lost sales and freshness) and four influencing factors (channel effect, demand variability, product perishability and shelf life visibility) for OCPI, through industry investigation, expert interviews and a systematic literature review. Treating OCPI as a complex adaptive system and considering its transaction costs, this paper formalizes the OCPI performance measures and their influencing factors in two statements and four propositions, which are then tested through numerical analysis with simulation.

Product waste, lost sales and freshness are identified as distinctive OCPI performance measures, which are influenced by product perishability, shelf life visibility, demand variability and channel effects. The OCPI sensitivity to those influencing factors is diverse, whereas those factors are found to moderate each other's effects.

To manage perishables more effectively, with less waste and lost sales for the business and fresher products for the consumer, omnichannel firms need to consider store and online channel requirements and strive to reduce demand variability, extend product shelf life and facilitate item-level shelf life visibility. While flexible logistics capacity and dynamic pricing can mitigate demand variability, the product shelf life extension needs modifications in product design, production, or storage conditions. OCPI executives can also increase the product shelf life visibility through advanced stock monitoring/tracking technologies (e.g. smart tags or more comprehensive barcodes), particularly for the online channel which demands fresher products.

This paper provides a novel theoretical view on perishables in omnichannel systems. It specifies the OCPI performance, beyond typical inventory policies for cost minimization, while discussing its sensitivity to operations and market factors.

Finding a rational approach to maintain a freshness of foods and perishable goods and saving their intrinsic attributes during a distribution of these products is one of the main issues for distribution and logistics companies. This paper aims to provide a framework for distribution of perishable goods which can be applied for real life situations.

This paper proposes a novel mathematical model for transportation inventory location routing problem. In addition, the paper addresses the impact of perishable goods age on the demand of final customers. The model is optimally solved for small- and medium-scale problems. Moreover, regarding to NP-hard nature of the proposed model, two simple and one hybrid metaheuristic algorithms are developed to cope with the complexity of problem in large scale problems.

Numerical examples with different scenarios and sensitivity analysis are conducted to investigate the performance of proposed algorithms and impacts of important parameters on optimal solutions. The results show the acceptable performance of proposed algorithms.

The authors formulate a novel mathematical model which can be applicable in perishable goods distribution systems In this regard, the authors consider lost sale which is proportional to age of products. A new hybrid approach is applied to tackle the problem and the results show the rational performance of the algorithm.

Inventory control models for perishable products have primarily used a FIFO issuing policy with the objective of minimizing the number of outdated units. This paper aims to develop a model to evaluate an issuing policy for a single product with a fixed shelf life in single echelon inventory system. The issuing policy considers the remaining shelf life of the in‐stock inventory and the expected time that the product will spend in inventory as the decision driver.

The model developed has an objective of maximizing expected revenue over time with a budget constraint. A heuristic algorithm is proposed to iteratively arrive at the best solution to the formulation. The heuristic is tested by employing a simulation model of the system.

The proposed heuristic is tested against both the FIFO and the random allocation approaches and found to be superior for all the in‐stock with remaining shelf life distribution means of above 40 percent. No significant performance differences were found for the three approaches for remaining shelf life distribution.

The research is focused on a single product with multiple expiration dates and further research is necessary to determine the best policies for the multi‐product multi‐expiration date environment where the items are substitutable..

Retailers stock items with multiple expiration dates. The customer, for obvious reasons, is more likely to choose the item with the longer remaining shelf life. Therefore, the supply to the retailer's shelves and issuing policies for making available the particular items to the customers affect product outdating and related costs. Revenues will be affected by the extent to which more can be charged for items with a longer remaining shelf life or by the impact of the remaining shelf life on demand. This paper provides for a practical approach to that end.

The proposed issuing policy has not been tested before and thus makes a contribution to the body of knowledge. The flexibility of using different values for acquisition costs, selling prices, salvage value and penalty functions is a particular strength of the proposed model. Moreover, its potential application to inventory control problems for a wide range of perishable products is substantial.

The objective of this paper is to propose a methodology based on random demand inventory models and dependence structures for a set of raw materials, referred to as “components”, used by food services that produce food rations referred to as “menus”.

The contribution margins of food services that produce menus are optimised using random dependent demand inventory models. The statistical dependence between the demand for components and/or menus is incorporated into the model through the multivariate Gaussian (or normal) distribution. The contribution margins are optimised by using probabilistic inventory models for each component and stochastic programming with a differential evolution algorithm.

When compared to the non-optimised system previously used by the company, the (average) expected contribution margin increases by 18.32 per cent when using a continuous review inventory model for groceries and uniperiodic models for perishable components (optimised system).

The multivariate modeling can be improved by using (a) other non-Gaussian (marginal) univariate probability distributions, by means of the copula method that considers more complex statistical dependence structures; (b) time-dependence, through autoregressive time-series structures and moving average; (c) random modelling of lead-time; and (d) demands for components with values equal to zero using zero-inflated or adjusted probability distribution.

Professional management of the supply chain allows the users to register data concerning component identification, demand, and stock levels to subsequently be used with the proposed methodology, which must be implemented computationally.

The proposed multivariate methodology allows it to describe demand dependence structures through inventory models applicable to components used to produce menus in food services.

Este trabajo propone una metodología basada en modelos de inventarios con demanda aleatoria y estructura de dependencia para un conjunto de materias primas, denominadas “componentes”, usadas por servicios de alimentación que producen raciones alimenticias denominadas “menús”.

Los margen de contribución de servicios de alimentación que producen menús son optimizados empleando modelos de inventarios con demandas aleatorias dependientes. La dependencia estadística entre demandas de componentes y/o menús es incorporada en el modelado mediante la distribución gaussiana (o normal) multivariada. La optimización de los márgenes de contribución se logra usando modelos de inventarios probabilísticos para cada componente y programación estocástica mediante el algoritmo de evolución diferencial.

El margen de contribución esperado (promedio) aumenta en un 18,32% usando modelos de inventario de revisión continua para abarrotes y modelos uniperiódicos para componentes perecederos (sistema optimizado), en relación al sistema no optimizado usado anteriormente por la compañía.

La metodología multivariada propuesta permite describir estructuras de dependencia de la demanda mediante modelos de inventario aplicables a componentes usados para producir menús en servicios de alimentación.

Una administración profesional de la gestión de la cadena de suministros permite registrar datos de la identificación del componente, su demanda y sus niveles de stock para ser usados posteriormente con la metodología propuesta, la que debe estar implementada computacionalmente.

El modelado multivariado puede ser mejorado (a) utilizando distribuciones probabilísticas univariadas (marginales) distintas a la gaussiana, mediante métodos de cópulas que recojan estructuras de dependencia estadística más complejas; (b) considerando demandas de componentes con valores iguales a cero, mediante distribuciones probabilísticas infladas en cero; (c) usando dependencia temporal, mediante estructuras de series de tiempo autorregresivas y de media móvil, y (d) modelando el lead-time en forma aleatoria.

The purpose of this paper is to maximize the average profit of the supply chain by calculating the order quantity, the number of shipments during the production time of the vendor, the number of shipments during the supply cycle of the vendor and the time when the retailer’s inventory level reaches to zero.

A production and inventory model for degrading commodities with stochastic demand and two-level partial trade credit in a supply chain is presented. The model’s applicability and the processes' feasibility for solving are verified by GAMS software with BARON.

The impact of the model’s parameters on the vendor and retailer’s average profit was found through sensitivity analysis. The effect of the model’s parameters on the supply chain’s average profit was also found. Moreover, the reasons for this effect were given.

First, decision-makers may use this model to increase the supply chain's average profit. Second, the proposed model takes a general form. Third, the policymakers can also adjust the model’s parameters according to their preferences to get the desired results.

First, this paper develops an inventory and production model for perishable goods. Second, it is believed that the demand is random because the demand is affected by many factors, which make the study more realistic. Third, this paper studies production and inventory problems from the supply chain perspective. Finally, the interest for partial trade credit is calculated. The interest caused by stochastic shortages is also considered and calculated.

Fresh product loss rates in supply chain operations are particularly high due to the nature of perishable products. The purpose of this paper is to maximize profit through the contract between retailer and supplier. The optimized prices for the retailer and the supplier, taking the fresh-keeping effort into consideration, are derived.

To address this issue, the authors consider a two-echelon supply chain consisting of a retailer and a supplier (i.e. wholesaler) for two scenarios: centralized and decentralized decision making. The authors start from investigating the optimal decision in the centralized supply chain and then comparing the results with those of the decentralized decision. Meanwhile, a fresh-keeping cost-sharing contract and a fresh-keeping cost- and revenue-sharing contract are designed. Numerical examples are provided, and managerial insights are discussed at the end.

The results show that the centralized decision is more profitable than the decentralized decision; a fresh product supply chain (FPSC) can only be coordinated through a fresh-keeping cost- and revenue-sharing contract; the optimal retail price, wholesale price and fresh-keeping effort can all be achieved; and the profit of a FPSC is positively related to consumers’ sensitivity to freshness and negatively correlated with their sensitivity to price.

This research is based on the assumption that demand is relatively stable. It has not addressed when demand is stochastic.

The findings would be useful for managers in fresh food sector in terms of how to deal with suppliers in order to maximize total profit while also provide freshest food to the customers.

Few studies have considered fresh-keeping effort as a decision variable in the modelling of supply chain. In this paper, a mathematical model for the fresh-keeping effort and for price decisions in a supply chain is developed. In particular, fresh-keeping cost-sharing contract and revenue-sharing contract are examined simultaneously in the study of the supply chain coordination problem.

The purpose of this paper is to investigate the impact of sharing and utilizing remaining shelf life (RSL) information from grocery stores by the use of age-based replenishment policies for perishables.

The performance is evaluated through a discrete event simulation model, which mirrors a part of one of Norway’s largest grocery retailer and uses their POS data to reflect a realistic demand pattern of 232 stores for one year.

The findings indicate that a current age-based replenishment policy (EWA policy) provides a significant improvement of 17.7 percent increase in availability for perishables with a shelf life between 4 and 11 days, but suffers from high inventory levels and only reduces waste by 3.4 percent compared to a base stock policy. A proposed adjustment to the EWA policy, EWA_SS, provides a more balanced performance in the conducted study with a reduction of 10.7 percent waste and 10.3 percent increase in availability by keeping the same average inventory level.

Sharing and utilizing RSL information for replenishment of perishables with a predetermined shelf life between 6 and 11 days can be beneficial, and could enable the replenishment processes to be automated. However, for products with longer shelf life, the benefits slowly diminish.

The study proposes a new age-based replenishment policy which in the conducted study showed a more balanced performance improvement, in both waste and availability, compared with previous replenishment policies.